1. Field of the Invention
The present invention relates to data processing and predictive process. More particularly, this invention relates to methods and systems for automatically predicting the airport capacity (i.e., arrival capacity—the number of aircraft that can land in a given amount of time or landing rate), the landing direction (the direction of the landing runways, i.e., north, south, east or west), the landing runway (the specific runway on which the aircraft is predicted to land) for a set of specified aircraft, during a specified time period, at a specified airport, based upon consideration of available aviation data regarding the weather, landings runways, airport configuration, departures, etc., to achieve a more accurate airport capacity, landing direction and landing runway prediction.
Additionally, a more accurate and objective prediction of the future airport capacity, landing direction and/or landing runway, using available aviation data, allows airlines, Air Traffic Control (ATC) and airports to better prepare and manage various assets, airline/airport functions and services at the specified airport, from a system perspective, to improve ATC, airline and airport operations and profitability.
Considered aviation data includes, but is not limited to current and predicted: weather (wind direction, wind speed, temperature, cloud ceiling, precipitation, visibility, etc.), airport design and layout, airport/ramp/taxiway congestion (i.e., number of aircraft at the airport taxing to or waiting for a gate, etc.), available runways, runway length, runway turnoffs, runway condition (dry, wet, icy, snow covered, etc.), available taxiways, taxiway conditions, arrival demand (number of aircraft predicted to land), departure demand (number of aircraft planned to depart the specified airport, etc.), ongoing airport maintenance, etc., during the specified time period.
ATC/airport/airline functions that could be improved through a process to better predict the airport capacity, landing direction and/or landing runway include, but are not limited to: safety, increased throughput (pulling some of the aircraft forward into unused airport capacity), arrival queue management (which aircraft should land first, etc.), gate assignment, passenger/cargo servicing (e.g., boarding, baggage, security, aircraft loading, in-flight meal service, in-flight, deplaning, interconnection to subsequent flights, bag sorting and delivery), aircraft servicing (e.g., fuel, lavatories, catering, cleaning, maintenance, deicing, bag/cargo loading), aircraft management (e.g., departure time, route of flight, landing sequence, landing time, crew assignments, weather, ride comfort, pushback or power back, taxi, takeoff, climb, cruise, descent, approach, landing, parking), gate management (gate availability, etc.), and support services (e.g., operation and maintenance of support vehicles, training, accounting, scheduling, payroll).
2. Description of the Related Art
The need for and advantages of real time prediction of future environments and events to better manage operational systems that optimize complex, multi-dimensional, interdependent processes have long been recognized. Thus, many complex methods and optimization systems have been developed. For example, see U.S. Pat. Nos. 5,321,605, 5,369,570, 5,890,133 and 5,953,707.
However, as applied to prediction of future events (i.e., capacity versus demand) within the aviation industry, such methods often have been subjective, accomplished manually, fragmentary or overly restrictive and have not addressed the overall prediction of future events so as to better optimize an airline's, airport's or ATC system's operational functions.
The reasons for this situation are complex and varied, but include considerations such as: dynamic nature of the weather, airport capabilities, the complex interdependence of the airlines and their use of shared airport facilities (i.e., common assets), local control of the movement of the aircraft, extensive governmental regulations and the impact of the airlines' unmanaged assets and aircraft flows to name a few.
To better understand the airline processes, FIG. 1 has been provided to indicate the current airline passenger and cargo movement processes, which commences with passenger ticketing, followed by airport arrival, passenger loading, aircraft servicing (e.g., loading of fuel, food, and cargo) and ending after arrival at the terminal gate and delivery of baggage and cargo. It is of interest to note that the core process within the airline industry is the movement of the aircraft. It moves off the gate, then works towards the next gate, is offloaded, serviced, loaded; only to move off the gate again.
Since almost all of the airline's other operational processes key off of the movement of aircraft, the core elements of an airline can be thought of as being managed from the center out as depicted in FIG. 2.
Like most businesses, the various ATC systems, local ATC facilities (ground, tower, departure, center, approach, etc.), airports and airlines are segmented into a number of distinct types of cost centers, business units or organizational entities, with the movement of the aircraft left to the individual pilots and Aviation Authorities Air Traffic Control system (ATC), managed by FAA in the United States, other Civil Aviation Authorities (CAA) or Air Navigation Service Providers (ANSPS) around the world.
Although most airline, airport and ATC processes are interdependent, current business practices within the airline industry promote the management of the individual assets independently by the individual asset managers without regard to system effects, i.e., future airport capabilities and requirements, so as to better meet the ATC systems, airport and/or airline's overall system goal of maximizing operational efficiency.
This has traditionally meant that the ATC system, airport and/or airline, have no real time, objective process, as defined within the present invention, to predict the airport capacity, landing direction and/or landing runway, into the future, using all of the available aviation data, and thus end up working to locally optimize individual elements of the aviation process, to the detriment of the overall aviation system.
There appear to be few current attempts by the various ATC systems, airports and/or airlines around the world to predict the airport capacity, landing direction and/or landing runway into the future so as to better manage the arriving and departing aircraft to better meet the airport's and airline's overall business and operational goals.
These independent actions for each of the airline's assets, without regard to system effects or airport capacity, landing direction or landing runway, lead to variance in the asset flows, thus assuring a random outcome of the airline's operational processes.
For illustrative purposes, one can compare the aircraft arrival flow into a busy airport to the actions of grade school children at the end of class. When the dismissal bell rings, if all of the students rush to the door, fighting to be the first one out, while the capacity of the door remains unchanged, the actual throughput of the door is lowered.
Conversely, if the students file out in an orderly and sequenced fashion, based on the actual available capacity (i.e., students per unit time that can fit through the door), the actual throughput of the door can be higher. And while the capacity of the door remains the same, predicting the capacity of the door, and then managing the flow through the door based on that capacity, the effective throughput of the door is higher. The same can be said for an airport.
The explanation of the effects of randomness can be found in the mathematics of queue theory (see FIG. 15), which states that as the demand approaches capacity the queue waiting time increases at a capacity proportional to the inverse of the difference between demand and capacity.
Although ATC systems, airports and airlines currently have available to them the required data on the positions of their and other aircraft assets (e.g., Aircraft Situational Display to Industry (ASDI), Flight Management System (FMS) downlinked data from the aircraft, Automatic Dependent Surveillance (ADS and ADS-B/C), the communications (e.g., Intranet for system coordination, radios for ground personnel and assets, ARINC Communications Addressing and Reporting System (ACARS) for aircraft communication) and future environmental capability (i.e., weather, runways availability and conditions, gates, etc.) to make more accurate predictions necessary to tactically manage these assets in a more efficient and profitable manner, they apparently lack the necessary objective, operational and business processes and methods to utilize this data.
Instead, current business practices involve the use of much of this data to analyze operational results and errors after the fact, using post processing methods.
And while there have been rudimentary attempts by the ATC process of the world's aviation authorities to report current airport capacity, landing direction and/or landing runways (FAA AAR, ATIS) and subjectively predict airport capacity, these have not been accurate enough or too late in the aircraft arrival process to allow better management of the aircraft arrival flow and airport actions.
One such example of one such attempt to predict future airport capacity by FAA can be found at http://www.fly.faa.gov/Products/AADC/aadc.html. Unfortunately, the data inputs to this system are subjective, based on the local ATC supervisor's view of the future and the airport capacity prediction is not very accurate.
Another example includes the 2008 “Decision Support Tool for Predicting Aircraft Arrival Rates, Ground Delay Programs, and Airport Delays from Weather Forecasts” study completed by Smith and Sherry (http://catsr.ite.gmu.edu/pubs/Smith_ICRATPaper.pdf). This paper describes a process of using the airport Terminal Aerodrome Forecast (TAF), as is done in the present invention. But this paper describing prior art stops short of addressing the other necessary components necessary to predict the airport capacity, landing direction, landing runway and runways available outlined herein, and incorporated in the present invention.
Another example of an arrival capacity prediction is provided by the FAA in the United States (US). The FAA System Command Center (SCC located in Herndon, Va.) currently posts current arrival rates and future arrival rates on the internet (http://www.fly.faa.gov/Products/AADC/aadc.html). Unfortunately, these arrival rates are manually and subjectively calculated and the predictive accuracy of the airport arrival capacity in the time horizon of the present invention (1 to 8 hours) is very low.
Despite the above noted prior art, the need continues to exist for improved methods and systems for predictive process (airport capacity, landing direction and/or landing runway).
The current ATC, airport and airline problems and limitations enumerated above are not intended to be exhaustive, but rather are among many which tend to impair the effect predictive aviation process. Other noteworthy problems may also exist; however, those presented above should be sufficient to demonstrate the need for improvements in airline business methods.
3. Objects and Advantages
There has been summarized above, rather broadly, the prior art that is related to the present invention in order that the context of the present invention may be better understood and appreciated. In this regard, it is instructive to also consider the objects and advantages of the present invention.
The present invention employs a novel method of utilizing the currently available predictive weather data and other predictive data concerning the specified airport to predict the airport capacity, landing direction and/or landing runway into the future. That is, the method of the present invention of utilizing the available aviation data (weather, airport environmental conditions, runway availability, arrival/departure demand, etc.) includes a substantially continuous process of monitoring and predicting both the location and time dependent characteristics of the available aviation data, and continuously predicting the airport capacity, landing direction and/or landing runway.
Effectively, the present invention works to predict the future airport capacity, landing direction and/or landing runway for a set of specified aircraft, during a specified time period, at a specified airport.
Such a system provides a user (i.e., ATC system, airport and/or airline) with the capabilities to continuously predict the future airport capacity, landing direction and/or landing runway for a set of specified aircraft, during a specified time period, at a specified airport so as to more accurately:                a) predict the time each aircraft will reach a specified airport,        b) determine the accuracy of the specified aviation data and the predictions based on that data,        c) compare the predicted unaltered trajectory of the specified assets to the airline's capabilities and business and operational goals,        d) build and analyze alternative scenarios to look for a solution that better meets the business and operational goals of a user airline (i.e., optimize the airline assets to increase profits), airport and ATC system,        e) display the chosen trajectory solution set for each of the airline assets to a system operator who can allow the present invention to automatically or manually accept/modify the proposed solution as required,        f) coordinate the targeted trajectory solution set with other users/aviation authorities and seeks authorization for use of the common assets as required,        g) communicate the assigned trajectories to each of the asset operators for each of the controlled assets,        h) continuously monitor the system to include the specified asset trajectories and other specified data and the airline's business and operational goals so as to identify any changes to the system or an action by one of the assets that prevents achievement of a assigned trajectory set, and        i) measure the airline's overall airline condition to determine if an updated solution set would better meet the operator's business and other goals.        j) implement and continuously monitor the updated trajectory solutions set as defined above.        
It is therefore an object of the present invention to provide a process, method and system for predicting the future airport capacity, landing direction and/or landing runway for a set of specified aircraft, during a specified time period, at a specified airport to overcome the limitations of the prior art described above.
It is another object of the present invention to present a method and system for the prediction of the future airport capacity, landing direction and/or landing runway for a set of specified aircraft, during a specified time period, at a specified airport, from a system perspective, that takes into consideration a wider array of parameters and factors not heretofore considered.
For example, such parameters and factors of aviation data may include, but is not limited to: weather (winds, temperature, cloud ceiling, precipitation, visibility, etc.), airport design and layout, airport/ramp/taxiway congestion (i.e., number of aircraft at the airport taxing to or waiting for a gate, etc.), available runways, runway length, runway turnoffs, runway condition (dry, wet, icy, snow covered, etc.), available taxiways, taxiway conditions, arrival demand (number of aircraft predicted to land), departure demand (number of aircraft planned to depart the specified airport, etc.), ongoing airport maintenance, etc.
It is a still further object of the present invention to provide a method and system (process or operating model) that analyzes larger amounts of tactical aviation related data/information and other factors simultaneously, identifies system constraints and problems as early as possible, uses said aviation data to predict airport capacity, landing direction and/or landing runway for a set of specified aircraft, during a specified time period, at a specified airport.
It is still a further object of the present invention to continuously measure the accuracy of said airport capacity, landing direction and/or landing runway prediction.
It is an additional object of the present invention to provide a method and system to continuously increase the accuracy of said airport capacity, landing direction and/or landing runway prediction by continuously monitoring the resultant accuracy of previous airport capacity, landing direction and/or landing runway predictions.
Such objects are different from the current art, which subjectively, manually, less accurately and sometimes never predicts the airport capacity, landing direction and/or landing runway.
These and other objects and advantages of the present invention will become readily apparent as the invention is better understood by reference to the accompanying drawings and the detailed description that follows.